Software Requirements to Support QoS in Collaborative M-Learning Activities

  • Didac Gil de La Iglesia
  • Marcelo Milrad
  • Jesper Andersson
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7493)


The use of collaborative activities in education has proven to be an effective way to enhance students’ learning outcomes by increasing their engagement and motivating discussions on the learning topics under exploration. In the field of Technology Enhanced Learning (TEL), the use of information and communication technologies has been extensively studied to provide alternative methods to support collaborative learning activities, combining different applications and tools. Mobile learning, a subset of TEL, has become a prominent area of research as it offers promising tools to enhance students’ collaboration and it provides alternative views for teaching and learning subject matter in relevant and authentic scenarios. While many studies have focused on the pedagogical opportunities provided by mobile technologies, fewer are the efforts looking at technological related aspects. Hardware and software issues in this field still remain as challenges that require a deeper level of study and analysis. This paper presents and discusses the findings of a deep analysis based on the outcomes of three mobile collaborative learning activities and their requirements. These results have helped us to identify a number of arising challenges that need to be addressed in order to warranty Quality of Service (QoS) in these collaborative M-learning activities. Moreover, the paper offers a view on current practices in M-learning activities, which evidences the lack of research addressing software engineering aspects in mobile collaborative learning.


Mobile Device Mobile Technology Mobile Learning Software Requirement Ubiquitous Learning Environment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Aseere, A.M., Millard, D.E., Gerding, E.H.: Ultra-Personalization and Decentralization: The Potential of Multi-Agent Systems in Personal and Informal Learning. In: Wolpers, M., Kirschner, P.A., Scheffel, M., Lindstaedt, S., Dimitrova, V. (eds.) EC-TEL 2010. LNCS, vol. 6383, pp. 30–45. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  2. 2.
    Chan, T., et al.: One-to-One Technology-Enhanced Learning: an Opportunity for Global Research Collaboration. Research and Practice in Technology Enhanced Learning 1(1), 3–29 (2006)CrossRefGoogle Scholar
  3. 3.
    Dillenbourg, P.: Collaborative Learning: Cognitive and Computational Approaches. Advances in Learning and Instruction Series. Elsevier Science, Inc. (1999)Google Scholar
  4. 4.
    Gil de la Iglesia, D.: Uncertainties in Mobile Learning applications: Software Architecture Challenges. Licentiate thesis, Linnaeus University (2012),
  5. 5.
    Gil de la Iglesia, D., et al.: Enhancing Mobile Learning Activities by the Use of Mobile Virtual Devices – Some Design and Implementation Issues. In: Proceedings of INCoS 2010, pp. 137–144. IEEE Computer Society (November 2010)Google Scholar
  6. 6.
    Gil de la Iglesia, D., et al.: Towards a Decentralized and Self-Adaptive System for M-Learning Applications. In: Proceedings of WMUTE 2012. IEEE Computer Society, Takamatsu (2012)Google Scholar
  7. 7.
    Hastings, D., McManus, H.: A framework for understanding uncertainty and its mitigation and exploitation in complex systems. IEEE Engineering Management Review 34(3), 1–19 (2006)CrossRefGoogle Scholar
  8. 8.
    Herrington, J., et al.: Using mobile technologies to develop new ways of teaching and learning. In: New Technologies, New Pedagogies: Mobile Learning in Higher Education, pp. 1–14. University of Wollongong (2009)Google Scholar
  9. 9.
    Herskovic, V., et al.: Modeling groupware for mobile collaborative work. In: Proc. of CSCWD 2009, pp. 384–389. IEEE Comp. Soc., Santiago (2009)Google Scholar
  10. 10.
    Jones, V., Jo, J.: Ubiquitous learning environment: An adaptive teaching system using ubiquitous technology. In: Proceedings of ASCILITE 2004. Beyond the Comfort Zone, pp. 468–474. Australiasian Society for Computers in Learning in Tertiary Education (2004)Google Scholar
  11. 11.
    Kukulska-Hulme, A., et al.: Innovation in Mobile Learning: A European Perspective BT. Inter. Journal of Mobile and Blended Learning 1(1), 13–35 (2009)CrossRefGoogle Scholar
  12. 12.
    Kurkovsky, S.: Multimodality in Mobile Computing and Mobile Devices: Methods for Adaptable Usability. IGI Global (2010)Google Scholar
  13. 13.
    Looi, C.K., et al.: Collaborative activities enabled by GroupScribbles (GS): An exploratory study of learning effectiveness. Computers & Education 54(1), 14–26 (2010)CrossRefGoogle Scholar
  14. 14.
    Neyem, A., Ochoa, S.F., Pino, J.A., Franco, D.: An Architectural Pattern for Mobile Groupware Platforms. In: Meersman, R., Herrero, P., Dillon, T. (eds.) OTM 2009 Workshops. LNCS, vol. 5872, pp. 401–410. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  15. 15.
    Neyem, A., et al.: A Patterns System to Coordinate Mobile Collaborative Applications. Group Decision and Negotiation 20(5), 563–592 (2011)CrossRefGoogle Scholar
  16. 16.
    Ogata, H., et al.: Computer supported ubiquitous learning environment for vocabulary learning. International Journal of Learning Technology 5(1), 5–24 (2010)CrossRefGoogle Scholar
  17. 17.
    Rogers, Y., et al.: Enhancing learning: a study of how mobile devices can facilitate sensemaking. Personal and Ubiquitous Computing 14(2), 111–124 (2010)CrossRefGoogle Scholar
  18. 18.
    Sharples, M., Taylor, J.: Towards a theory of mobile learning. In: Proc. of mLearn 2005 (2005)Google Scholar
  19. 19.
    Sharples, M., et al.: Mobile Learning. In: Balacheff, N., et al. (eds.) Technology-Enhanced Learning, pp. 233–249. Springer, Netherlands (2009)CrossRefGoogle Scholar
  20. 20.
    Tarkoma, S.: Mobile Middleware: Architecture, Patterns and Practice. John Wiley & Sons Inc. (2009)Google Scholar
  21. 21.
    Tuulos, V.H., Scheible, J., Nyholm, H.: Combining Web, Mobile Phones and Public Displays in Large-Scale: Manhattan Story Mashup. In: LaMarca, A., Langheinrich, M., Truong, K.N. (eds.) Pervasive 2007. LNCS, vol. 4480, pp. 37–54. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  22. 22.
    Weyns, D., et al.: A Survey on Formal Methods in Self-Adaptive Systems. In: Proc. of FMSAS 2012 (2012)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Didac Gil de La Iglesia
    • 1
  • Marcelo Milrad
    • 1
  • Jesper Andersson
    • 1
  1. 1.DFMLinnaeus UniversityVäxjöSweden

Personalised recommendations